Sudipta Banerjee
      

Contact Details:

Center for Visual Information Technolgy (CVIT)

International Institute of Information Technology (IIIT-H)

Gachibowli, Hyderabad, India

sudipta.b@iiit.ac.in   banerjeesudipta30@gmail.com

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About me

  • Currently appointed as an Assistant Professor associated with the CVIT lab at International Institute of Information Technology (IIIT-H), Hyderabad, India
  • Was a Research Associate (Postdoctoral scholar) in the iPRoBe lab supervised by Professor Arun Ross at Michigan State University
  • Completed Doctoral of Philosophy in Computer Science from Michigan State University, East Lansing, United States in 2020. Primary adviser was Professor Arun Ross, the director of iPRoBe lab. Dissertation title: "Digital Image Forensics in the Context of Biometrics"
  • Completed Masters in Engineering in Electronics and Telecommunication Engineering from Jadavpur University, Kolkata, India in 2014 where I was a member of the IVPR group led by Dr. Ananda Shankar Chowdhury
  • Completed Bachelors in Technology in Electronics and Communication Engineering from the West Bengal University of Technology (now renamed as MAKAUT), Kolkata, India in 2011




Research Interests

Detecting face morphs and disentangling identities used in creating morph: Face morphing strategically mixes two or more face images such that the composite image matches successfully to all constituents in terms of biometric utility. Therefore, a morphed face image can be utilized adversarially ina na identification document and poses a security threat. We proposed a conditional identity disentanglement network using a GAN that not only performs differential face morph detection guided by a trusted non-morphed reference image but alos recovers the second identity used in creating the morph. Check this video to know more about Conditional Identity Disentanglement for Differential Face Morph Detection!

Camera sensor identification for biometric images: Photo Response Non-Uniformity (PRNU) has been successfully utilized for sensor identification in the literature and is important in the context of image forensics. PRNU manifests as a consequence of artifacts associated with the sensor fabrication process. In iris biometrics, the images are captured using iris sensors typically operating in the near-infrared spectrum, which differ from conventional RGB sensors employed in the camera. Also, the iris images can be subjected to some pre-processing schemes, such as photometric modifications to aid in iris recognition. We evaluate different PRNU schemes in the context of iris sensor identification. We further analyze the impact of photometric transformations known to improve iris recognition performance on PRNU based sensor identification. In many image forensic applications, one can implicitly link the camera with the photographer. This raises privacy concerns, which can be mitigated via sensor de-identification. In this context, we deliberately perturb the image such that the PRNU based sensor classifier incorrectly assigns the modified image to a different sensor but without compromising the utility of the images. In our work, we aim to confound the iris sensor classifier, whilst preserving the iris recognition performance. Check this video to know more about Exploring Vulnerabilities of PRNU-based Camera Fingerprinting!

Deducing the structure of evolution between a set of near-duplicate images: An image can undergo a sequence of photometric or geometric transformations such as, brightness and contrast adjustment, rotation, scaling, etc. to yield a set of near-duplicate images related to each other. Deduction of the hierarchical structure of evolution can be used in the context of image forensics and it is particularly useful in current times due to plethora of image editing tools and software. We develop a method which accepts as input a set of near-duplicate images, and estimate the pair-wise transformation parameters using a parameterized model that employs basis functions. We further utilize the estimated parameters to obtain the relationship and depict it in the form of an Image Phylogeny Tree. The IPT is a directed acyclic graph which indicates the root node (original image) and the child nodes (transformed images) and how they are related to each other. Our method works on near-duplicate face and iris images that have undergone manual as well as automated transformations.

Cyberattack pattern analysis: Defacement of webpages via insertion of graphics and text leads to denial of service attacks and causes financial setbacks to commercial websites. Cyberattacks can be malicious or inocuous depending on the intent of the attacker. Some attackers target specific websites and are active across multiple years. In this work, we perform a longitudinal analysis on web defacement data curated across six years (2012-2017) to detect any discernible pattern in the cyberattacks using machine learning algorithms. This is an interdisciplinary project in collaboration with the Department of Communication, Arts and Sciences and School of Criminal Justice to help understand the human factors in cybercrimes.







Publications

  • S. Banerjee and A. Ross, "Conditional Identity Disentanglement for Differential Face Morph Detection," International Joint Conference on Biometrics (IJCB), Shenzhen, China [Virtual], 2021. URL: link to paper

  • S. Banerjee, T. Swearingen, R. Shillair, J. Bauer, T. Holt and A. Ross, "Using Machine Learning to Examine Cyberattack Motivations on Web Defacement Data," Social Science Computer Review (SSCR), 2021. URL: link to paper

  • A. Ross, S. Banerjee and A. Chowdhury, "Security in Smart Cities: A Brief Review of Digital Forensic Schemes for Biometric Data," Pattern Recognition Letters (PRL), Vol. 138, pp. 346-354, 2020. URL: link to paper

  • S. Banerjee and A. Ross, "One Shot Representational Learning for Joint Biometric and Device Authentication," 25th International Conference on Pattern Recognition (ICPR), Milan, Italy [Virtual], 2020. URL: link to paper

  • S. Banerjee and A. Ross, "Face Phylogeny Tree Using Basis Functions," IEEE Transactions on Biometrics, Behavior and Identity Science (T-BIOM), Vol.2, Issue 4, pp. 310-325, 2020. URL: link to paper

  • S. Banerjee, T. Swearingen, R. Shillair, J. Bauer, T. Holt and A. Ross, "Analysis of Cyberattack Patterns Across Longitudinal Data", 2nd Annual conference on Human Factor in Cybercrime, Amsterdam, Netherlands, 2019.

  • S. Banerjee and A. Ross, "Face Phylogeny Tree: Deducing Relationships Between Near-Duplicate Face Images Using Legendre Polynomials and Radial Basis Functions," 10th IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS), Florida, USA, 2019. (Best Paper and Best Poster Award) URL: link to paper

  • S. Banerjee and A. Ross, "Smartphone Camera De-identification while Preserving Biometric Utility," 10th IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS), Florida, USA, 2019. URL: link to paper

  • A. Ross, S. Banerjee, C. Chen, A. Chowdhury, V. Mirjalili, R. Sharma, T. Swearingen, and S. Yadav, "Some Research Problems in Biometrics: The Future Beckons", 12th IAPR International Conference on Biometrics (ICB), Crete, Greece, 2019. URL: link to paper

  • S. Banerjee, V. Mirjalili and A. Ross, "Spoofing PRNU patterns of Iris Sensors while Preserving Iris Recognition," 5th International Conference on Identity, Security and Behavior Analysis (ISBA), Hyderabad, India, 2019. (Best Paper Award) URL: link to paper

  • S. Banerjee and A. Ross, "Impact of Photometric Transformations on PRNU Estimation Schemes: A Case Study Using Near Infrared Ocular Images," 6th International Workshop on Biometrics and Forensics (IWBF), Sassari, Italy, 2018. (Best Student Paper Award) URL: link to paper

  • S. Banerjee and A. Ross, "Computing an Image Phylogeny Tree from Photometrically Modified Iris Images," IEEE International Joint Conference on Biometrics (IJCB), Denver, USA, 2017, pp. 618-626.doi: 10.1109/BTAS.2017.8272749. URL: link to paper

  • S. Banerjee and A. Ross, "From Image to Sensor: Comparative Evaluation of Multiple PRNU Estimation Schemes for Identifying Sensors from NIR Iris Images," 5th International Workshop on Biometrics and Forensics (IWBF), Coventry, UK, 2017, pp. 1-6.doi:10.1109/IWBF.2017.7935081. URL: link to paper

  • V. N. Gangapure, S. Banerjee and A. S. Chowdhury, “Steerable Local Frequency Based Multispectral Multifocus Image Fusion”, Information Fusion, Volume 23, 2015 Pages 99-115, ISSN 1566-2535 URL: link to paper

  • S. Banerjee, V. N. Gangapure and A. S. Chowdhury, “Multispectral Multifocus Image Fusion with Guided Steerable Frequency and Improved Saliency”, In Proceedings of the Indian Conference on Computer Vision Graphics and Image Processing (ICVGIP '14). ACM, New York, NY, USA, Article 9, 8 pages. URL: link to paper

 

 

Resume

Resume available here.